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BackgroundClarifying the relationship between depression symptoms and cardiometabolic and related health could clarify risk factors and treatment targets. The objective of this study was to assess whether depression symptoms in midlife are associated with the subsequent onset of cardiometabolic health problems.MethodsThe study sample comprised 787 male twin veterans with polygenic risk score data who participated in the Harvard Twin Study of Substance Abuse (‘baseline’) and the longitudinal Vietnam Era Twin Study of Aging (‘follow-up’). Depression symptoms were assessed at baseline [mean age 41.42 years (s.d. = 2.34)] using the Diagnostic Interview Schedule, Version III, Revised. The onset of eight cardiometabolic conditions (atrial fibrillation, diabetes, erectile dysfunction, hypercholesterolemia, hypertension, myocardial infarction, sleep apnea, and stroke) was assessed via self-reported doctor diagnosis at follow-up [mean age 67.59 years (s.d. = 2.41)].ResultsTotal depression symptoms were longitudinally associated with incident diabetes (OR 1.29, 95% CI 1.07–1.57), erectile dysfunction (OR 1.32, 95% CI 1.10–1.59), hypercholesterolemia (OR 1.26, 95% CI 1.04–1.53), and sleep apnea (OR 1.40, 95% CI 1.13–1.74) over 27 years after controlling for age, alcohol consumption, smoking, body mass index, C-reactive protein, and polygenic risk for specific health conditions. In sensitivity analyses that excluded somatic depression symptoms, only the association with sleep apnea remained significant (OR 1.32, 95% CI 1.09–1.60).ConclusionsA history of depression symptoms by early midlife is associated with an elevated risk for subsequent development of several self-reported health conditions. When isolated, non-somatic depression symptoms are associated with incident self-reported sleep apnea. Depression symptom history may be a predictor or marker of cardiometabolic risk over decades.

Because longitudinal studies of aging typically lack cognitive data from earlier ages, it is unclear how general cognitive ability (GCA) changes throughout the life course. In 1173 Vietnam Era Twin Study of Aging (VETSA) participants, we assessed young adult GCA at average age 20 and current GCA at 3 VETSA assessments beginning at average age 56. The same GCA index was used throughout. Higher young adult GCA and better GCA maintenance were associated with stronger specific cognitive abilities from age 51 to 73. Given equivalent GCA at age 56, individuals who had higher age 20 GCA outperformed those whose GCA remained stable in terms of memory, executive function, and working memory abilities from age 51 to 73. Thus, paradoxically, despite poorer maintenance of GCA, high young adult GCA still conferred benefits. Advanced predicted brain age and the combination of elevated vascular burden and APOE-ε4 status were associated with poorer maintenance of GCA. These findings highlight the importance of distinguishing between peak and current GCA for greater understanding of cognitive aging.

We examined the influence of lifestyle on brain aging after nearly 30 years, and tested the hypothesis that young adult general cognitive ability (GCA) would moderate these effects. In the community-dwelling Vietnam Era Twin Study of Aging (VETSA), 431 largely non-Hispanic white men completed a test of GCA at mean age 20. We created a modifiable lifestyle behavior composite from data collected at mean age 40. During VETSA, MRI-based measures at mean age 68 included predicted brain age difference (PBAD), Alzheimer's disease (AD) brain signature, and abnormal white matter scores. There were significant main effects of young adult GCA and lifestyle on PBAD and the AD signature (ps ≤ 0.012), and a GCA-by-lifestyle interaction on both (ps ≤ 0.006). Regardless of GCA level, having more favorable lifestyle behaviors predicted less advanced brain age and less AD-like brain aging. Unfavorable lifestyles predicted advanced brain aging in those with lower age 20 GCA, but did not affect brain aging in those with higher age 20 GCA. Targeting early lifestyle modification may promote dementia risk reduction, especially among lower reserve individuals.

OBJECTIVES Major Depressive Disorder (MDD) is a complex neuropsychiatric disease with known genetic associations, but without known links to rare variation in the human genome. Here we aim to identify rare genetic variants associated with MDD using deep whole-genome sequencing data in an independent population. METHODS We report the sequencing of 1,688 whole genomes in a large sample of male-male Veteran twins. Depression status was classified based on a structured diagnostic interview according to DSM-III-R diagnostic criteria. Searching only rare variants in genomic regions from recent GWAS on MDD, we used the optimised sequence kernel association test and Fisher's Exact test to fine map loci associated with severe depression. RESULTS Our analysis identified one gene associated with severe depression, basic helix loop helix e22 (PAdjusted = 0.03) via SKAT-O test between unrelated severely depressed cases compared to unrelated non-depressed controls. The same gene BHLHE22 had a non-silent variant rs13279074 (PAdjusted = 0.032) based on a single variant Fisher's Exact test between unrelated severely depressed cases compared to unrelated non-depressed controls. CONCLUSION The gene BHLHE22 shows compelling genetic evidence of directly impacting the severe depression phenotype. Together these results advance understanding of the genetic contribution to major depressive disorder in a new cohort and link a rare variant to severe forms of the disorder.

Background and aims Smoking is associated with increased risk for brain aging/atrophy and dementia. Few studies have examined early associations with brain aging. This study aimed to measure whether adult men with a history of heavier smoking in early mid-life would have older than predicted brain age 16-28 years later. Design Prospective cohort observational study, utilizing smoking pack years data from average age 40 (early mid-life) predicting predicted brain age difference scores (PBAD) at average ages 56, 62 (later mid-life) and 68 years (early old age). Early mid-life alcohol use was also evaluated. Setting Population-based United States sample. Participants/cases Participants were male twins of predominantly European ancestry who served in the United States military between 1965 and 1975. Structural magnetic resonance imaging (MRI) began at average age 56. Subsequent study waves included most baseline participants; attrition replacement subjects were added at later waves. Measurements Self-reported smoking information was used to calculate pack years smoked at ages 40, 56, 62, and 68. MRIs were processed with the Brain-Age Regression Analysis and Computation Utility software (BARACUS) program to create PBAD scores (chronological age-predicted brain age) acquired at average ages 56 (n = 493; 2002-08), 62 (n = 408; 2009-14) and 68 (n = 499; 2016-19). Findings In structural equation modeling, age 40 pack years predicted more advanced age 56 PBAD [β = -0.144, P = 0.012, 95% confidence interval (CI) = -0.257, -0.032]. Age 40 pack years did not additionally predict PBAD at later ages. Age 40 alcohol consumption, but not a smoking × alcohol interaction, predicted more advanced PBAD at age 56 (β = -0.166, P = 0.001, 95% CI = -0.261, -0.070) with additional influences at age 62 (β = -0.115, P = 0.005, 95% CI = -0.195, -0.036). Age 40 alcohol did not predict age 68 PBAD. Within-twin-pair analyses suggested some genetic mechanism partially underlying effects of alcohol, but not smoking, on PBAD. Conclusions Heavier smoking and alcohol consumption by age 40 appears to predict advanced brain aging by age 56 in men.

Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data ( http://www.brainchart.io/ ). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes., R.A.I.B. was supported by a British Academy Postdoctoral fellowship and by the Autism Research Trust. J. Seidlitz was supported by NIMH T32MH019112-29 and K08MH120564. S.R.W. was funded by UKRI Medical Research Council MC_UU_00002/2 and was supported by the NIHR Cambridge Biomedical Research Centre (BRC-1215-20014). E.T.B. was supported by an NIHR Senior Investigator award and the Wellcome Trust collaborative award for the Neuroscience in Psychiatry Network. A.F.A.-B. was supported by NIMH K08MH120564. Data were curated and analysed using a computational facility funded by an MRC research infrastructure award (MR/M009041/1) to the School of Clinical Medicine, University of Cambridge and supported by the mental health theme of the NIHR Cambridge Biomedical Research Centre.

Funding Information: This work was supported by the National Institute of Mental Health / U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium ), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience . Statistical analyses were carried out on the LISA/Genetic Cluster Computer ( https://userinfo.surfsara.nl/systems/lisa ) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. Funding Information: MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc., RallyPoint Networks, Inc., Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled “Genotype-guided dosing of opioid agonists,” filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Funding Information: This work was supported by the National Institute of Mental Health/ U.S. Army Medical Research and Development Command (Grant No. R01MH106595 [to CMN, IL, MBS, KJRe, and KCK], National Institutes of Health (Grant No. 5U01MH109539 to the Psychiatric Genomics Consortium), and Brain & Behavior Research Foundation (Young Investigator Grant [to KWC]). Genotyping of samples was provided in part through the Stanley Center for Psychiatric Genetics at the Broad Institute supported by Cohen Veterans Bioscience. Statistical analyses were carried out on the LISA/Genetic Cluster Computer (https://userinfo.surfsara.nl/systems/lisa) hosted by SURFsara. This research has been conducted using the UK Biobank resource (Application No. 41209). This work would have not been possible without the financial support provided by Cohen Veterans Bioscience, the Stanley Center for Psychiatric Genetics at the Broad Institute, and One Mind. This material has been reviewed by the Walter Reed Army Institute of Research. There is no objection to its presentation and/or publication. The opinions or assertions contained herein are the private views of the authors and are not to be construed as official or as reflecting true views of the U.S. Department of the Army or the Department of Defense. We thank the investigators who comprise the PGC-PTSD working group and especially the more than 206,000 research participants worldwide who shared their life experiences and biological samples with PGC-PTSD investigators. We thank Mark Zervas for his critical input. Full acknowledgments are in Supplement 1. MBS has in the past 3 years received consulting income from Actelion, Acadia Pharmaceuticals, Aptinyx, Bionomics, BioXcel Therapeutics, Clexio, EmpowerPharm, GW Pharmaceuticals, Janssen, Jazz Pharmaceuticals, and Roche/Genentech and has stock options in Oxeia Biopharmaceuticals and Epivario. In the past 3 years, NPD has held a part-time paid position at Cohen Veterans Bioscience, has been a consultant for Sunovion Pharmaceuticals, and is on the scientific advisory board for Sentio Solutions for unrelated work. In the past 3 years, KJRe has been a consultant for Datastat, Inc. RallyPoint Networks, Inc. Sage Pharmaceuticals, and Takeda. JLM-K has received funding and a speaking fee from COMPASS Pathways. MU has been a consultant for System Analytic. HRK is a member of the Dicerna scientific advisory board and a member of the American Society of Clinical Psychopharmacology Alcohol Clinical Trials Initiative, which during the past 3 years was supported by Alkermes, Amygdala Neurosciences, Arbor Pharmaceuticals, Dicerna, Ethypharm, Indivior, Lundbeck, Mitsubishi, and Otsuka. HRK and JG are named as inventors on Patent Cooperative Treaty patent application number 15/878,640, entitled ?Genotype-guided dosing of opioid agonists,? filed January 24, 2018. RP and JG are paid for their editorial work on the journal Complex Psychiatry. OAA is a consultant to HealthLytix. All other authors report no biomedical financial interests or potential conflicts of interest. Publisher Copyright: © 2021 Society of Biological Psychiatry Background: Posttraumatic stress disorder (PTSD) is heritable and a potential consequence of exposure to traumatic stress. Evidence suggests that a quantitative approach to PTSD phenotype measurement and incorporation of lifetime trauma exposure (LTE) information could enhance the discovery power of PTSD genome-wide association studies (GWASs). Methods: A GWAS on PTSD symptoms was performed in 51 cohorts followed by a fixed-effects meta-analysis (N = 182,199 European ancestry participants). A GWAS of LTE burden was performed in the UK Biobank cohort (N = 132,988). Genetic correlations were evaluated with linkage disequilibrium score regression. Multivariate analysis was performed using Multi-Trait Analysis of GWAS. Functional mapping and annotation of leading loci was performed with FUMA. Replication was evaluated using the Million Veteran Program GWAS of PTSD total symptoms. Results: GWASs of PTSD symptoms and LTE burden identified 5 and 6 independent genome-wide significant loci, respectively. There was a 72% genetic correlation between PTSD and LTE. PTSD and LTE showed largely similar patterns of genetic correlation with other traits, albeit with some distinctions. Adjusting PTSD for LTE reduced PTSD heritability by 31%. Multivariate analysis of PTSD and LTE increased the effective sample size of the PTSD GWAS by 20% and identified 4 additional loci. Four of these 9 PTSD loci were independently replicated in the Million Veteran Program. Conclusions: Through using a quantitative trait measure of PTSD, we identified novel risk loci not previously identified using prior case-control analyses. PTSD and LTE have a high genetic overlap that can be leveraged to increase discovery power through multivariate methods. publishersversion published

Background Stage 1 of the National Institute on Aging–Alzheimer’s Association’s proposed Alzheimer’s disease continuum is defined as amyloid-β (Aβ) positive but cognitively normal. Identifying at-risk individuals before Aβ reaches pathological levels could have great benefits for early intervention. Although Aβ levels become abnormal long before severe cognitive impairments appear, increasing evidence suggests that subtle cognitive changes may begin early, potentially before Aβ surpasses the threshold for abnormality. We examined whether baseline cognitive performance would predict progression from normal to abnormal levels of Aβ. Methods We examined the association of baseline cognitive composites (Preclinical Alzheimer Cognitive Composite, Alzheimer’s Disease Neuroimaging Initiative (ADNI) memory factor composite) with progression to Aβ positivity in 292 nondemented, Aβ-negative ADNI participants. Additional analyses included continuous cerebrospinal fluid biomarker levels to examine the effects of subthreshold pathology. Results Forty participants progressed to Aβ positivity during follow-up. Poorer baseline performance on both cognitive measures was significantly associated with increased odds of progression. More abnormal levels of baseline cerebrospinal fluid phosphorylated tau and subthreshold Aβ were associated with increased odds of progression to Aβ positivity. Nevertheless, baseline ADNI memory factor composite performance predicted progression even after controlling for baseline biomarker levels and APOE genotype (Preclinical Alzheimer Cognitive Composite was trend level). Survival analyses were largely consistent: controlling for baseline biomarker levels, baseline Preclinical Alzheimer Cognitive Composite still significantly predicted progression time to Aβ positivity (ADNI memory factor composite was trend level). Conclusions The possibility of intervening before Aβ reaches pathological levels is of obvious benefit. Low-cost, noninvasive cognitive measures can be informative for determining who is likely to progress to Aβ positivity, even after accounting for baseline subthreshold biomarker levels.

BackgroundNo studies have examined the longitudinal association between the persistence of pain and its relationship to cognitive problems in older adults. The objective of this study was to examine how the persistent of pain associates with cognitive performance, cognitive impairment, and subjective memory decline.MethodsAcross 10 biennial waves, 8515 adults ages 65 and over were assessed from the Health and Retirement Study (Mage =74.17, SD=6.87, 59.2% female). At each wave, individuals were asked to report on pain presence, and if present, rate its intensity and interference with daily activities such as housework or chores. Using running frequencies or averages, we calculated the persistence of pain using these three pain measures. Cognition was assessed using cognitive performance and different cognitive impairment cutoffs. Incident subjective memory decline was additionally measured as new self-reported memory change in the last 2 years. General estimating equations examined concurrent associations between persistence of pain and cognitive variables, adjusting for demographics, depressive symptoms, and medical comorbidities.ResultsPersistence of pain presence was associated with an increased risk of cognitive impairment. Only persistence of pain interference, not pain intensity, was significantly associated with poorer cognitive performance or being classified as cognitively impaired. For every 2 years, persistence of pain interference was associated with 21% increased odds of cognitive impairment. Only one of three pain variables was related to incident subjective memory decline.ConclusionsPersistence of pain is associated with poorer cognitive performance in community-dwelling older adults, especially when involving ongoing interference in chores and work. Facilitating pain management might be important for helping to maintain later-life cognition and reduce dementia risk.

The locus coeruleus (LC) is a brainstem region involved in regulating pain. Chronic pain is common in older adulthood, but no studies have examined its association with the LC in humans. We used neuromelanin-sensitive imaging to study differences in LC integrity in older adults with and without chronic pain. Chronic pain was assessed in community-dwelling men from the Vietnam Era Twin Study of Aging (VETSA) in 3 study waves covering an average of 12 years. Pain was self-reported on the SF-36 Bodily Pain Scale. Chronic pain was defined as moderate to severe pain severity at the current and at least one prior wave; 17% had chronic pain (n=80). At the third wave, 481 participants (mean age=67.57) underwent neuromelanin-sensitive MRI scans from which we calculated an LC contrast-to-noise ratio (LCCNR) – an index of LC integrity. We examined associations between chronic pain and LCCNR (in the rostral LC and caudal regions) with generalized estimating equations after adjusting for age, race, education, depressive symptoms, medical comorbidities, and opioid medication use. Individuals with chronic pain had .35 standard deviation lower rostral LCCNR (95% CI: -.62 to -.05) compared to those without chronic pain. No differences in the caudal LCCNR were detected. Chronic pain was associated with decreased rostral LC integrity in older adults. Differences in the rostral LC, rather than caudal LC, suggest the association between lower LC integrity and chronic pain may be related to pain processing in cortical regions where rostral LC projections typically connect.

Magnetic resonance imaging data are being used in statistical models to predicted brain ageing (PBA) and as biomarkers for neurodegenerative diseases such as Alzheimers Disease. Despite their increasing application, the genetic and environmental etiology of global PBA indices is unknown. Likewise, the degree to which genetic influences in PBA are longitudinally stable and how PBA changes over time are also unknown. We analyzed data from 734 men from the Vietnam Era Twin Study of Aging with repeated MRI assessments between the ages 52 to 72 years. Biometrical genetic analyses twin models revealed significant and highly correlated estimates of additive genetic heritability ranging from 59% to 75%. Multivariate longitudinal modelling revealed that covariation between PBA at different timepoints could be explained by a single latent factor with 73% heritability. Our results suggest that genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and are largely explained by common genetic influences. HighlightsWe explored the genetic and environmental etiology of MRI-based predicted brain age (PBA) in a longitudinal sample of males starting in midlife. Genetic influences on PBA are detectable in midlife or earlier, are longitudinally very stable, and largely explained by common genetic influences.

A growing number of studies have examined alterations in white matter organization in people with posttraumatic stress disorder (PTSD) using diffusion MRI (dMRI), but the results have been mixed, which may be partially due to relatively small sample sizes among studies. Altered structural connectivity may be both a neurobiological vulnerability for, and a result of, PTSD. In an effort to find reliable effects, we present a multi-cohort analysis of dMRI metrics across 3,047 individuals from 28 cohorts currently participating in the PGC-ENIGMA PTSD working group (a joint partnership between the Psychiatric Genomics Consortium and the Enhancing NeuroImaging Genetics through Meta-Analysis consortium). Comparing regional white matter metrics across the full brain in 1,426 individuals with PTSD and 1,621 controls (2174 males/873 females) between ages 18–83, 92% of whom were trauma-exposed, we report associations between PTSD and disrupted white matter organization measured by lower fractional anisotropy (FA) in the tapetum region of the corpus callosum (Cohen’s d=−0.11, p=0.0055). The tapetum connects the left and right hippocampus, structures for which structure and function have been consistently implicated in PTSD. Results remained significant/similar after accounting for the effects of multiple potentially confounding variables: childhood trauma exposure, comorbid depression, history of traumatic brain injury, current alcohol abuse or dependence, and current use of psychotropic medications. Our results show that PTSD may be associated with alterations in the broader hippocampal network.

Background: Although not strongly correlated with current objective cognitive ability, subjective cognitive decline (SCD) is a risk factor for Alzheimer’s disease. Most studies focus on SCD in relation to future decline rather than objective prior decline that it purportedly measures. Objective: We evaluated whether self-report of cognitive decline—as a continuous measure—corresponds to objectively-assessed episodic memory and executive function decline across the same period. Methods: 1,170 men completed the Everyday Cognition Questionnaire (ECog) at mean age 68 assessing subjective changes in cognitive ability relative to 10 years prior. A subset had mild cognitive impairment (MCI), but MCI was diagnosed without regard to subjective decline. Participants completed up to 3 objective assessments of memory and executive function (M = 56, 62, and 68 years). Informant-reported ECogs were completed for 1,045 individuals. Analyses controlled for depression and anxiety symptoms assessed at mean age 68. Results: Participant-reported ECog scores were modestly associated with objective decline for memory (β= –0.23, 95%CI [–0.37, –0.10]) and executive function (β= –0.19, 95%CI [–0.33, –0.05]) over the same time period. However, these associations were nonsignificant after excluding MCI cases. Results were similar for informant ratings. Participant-rated ECog scores were more strongly associated with concurrent depression and anxiety symptoms, (β= 0.44, 95%CI [0.36, 0.53]). Conclusion: Continuous SCD scores are correlated with prior objective cognitive changes in non-demented individuals, though this association appears driven by individuals with current MCI. However, participants’ current depression and anxiety ratings tend to be strongly associated with their SCD ratings. Thus, what primarily drives SCD ratings remains unclear.

Neuroimaging signatures based on composite scores of cortical thickness and hippocampal volume predict progression from mild cognitive impairment to Alzheimer’s disease. However, little is known about the ability of these signatures among cognitively normal adults to predict progression to mild cognitive impairment. Towards that end, a signature sensitive to microstructural changes that may predate macrostructural atrophy should be useful. We hypothesized that: (i) a validated MRI-derived Alzheimer’s disease signature based on cortical thickness and hippocampal volume in cognitively normal middle-aged adults would predict progression to mild cognitive impairment; and (ii) a novel grey matter mean diffusivity signature would be a better predictor than the thickness/volume signature. This cohort study was part of the Vietnam Era Twin Study of Aging. Concurrent analyses compared cognitively normal and mild cognitive impairment groups at each of three study waves (ns = 246–367). Predictive analyses included 169 cognitively normal men at baseline (age = 56.1, range = 51–60). Our previously published thickness/volume signature derived from independent data, a novel mean diffusivity signature using the same regions and weights as the thickness/volume signature, age, and an Alzheimer’s disease polygenic risk score were used to predict incident mild cognitive impairment an average of 12 years after baseline (follow-up age = 67.2, range = 61–71). Additional analyses adjusted for predicted brain age difference scores (chronological age minus predicted brain age) to determine if signatures were Alzheimer-related and not simply ageing-related. In concurrent analyses, individuals with mild cognitive impairment had higher (worse) mean diffusivity signature scores than cognitively normal participants, but thickness/volume signature scores did not differ between groups. In predictive analyses, age and polygenic risk score yielded an area under the curve of 0.74 (sensitivity = 80.00%; specificity = 65.10%). Prediction was significantly improved with addition of the mean diffusivity signature (area under the curve = 0.83; sensitivity = 85.00%; specificity = 77.85%; P = 0.007), but not with addition of the thickness/volume signature. A model including both signatures did not improve prediction over a model with only the mean diffusivity signature. Results held up after adjusting for predicted brain age difference scores. The novel mean diffusivity signature was limited by being yoked to the thickness/volume signature weightings. An independently derived mean diffusivity signature may thus provide even stronger prediction. The young age of the sample at baseline is particularly notable. Given that the brain signatures were examined when participants were only in their 50 s, our results suggest a promising step towards improving very early identification of Alzheimer’s disease risk and the potential value of mean diffusivity and/or multimodal brain signatures., Among cognitively normal adults in their 50 s, an Alzheimer’s brain signature based on grey matter mean diffusivity—but not cortical thickness—significantly improved prediction of progression to mild cognitive impairment 12 years later. Grey matter mean diffusivity signatures may be sensitive to Alzheimer-related changes as early as middle age., Graphical Abstract Graphical Abstract

Contains fulltext : 235500.pdf (Publisher’s version ) (Open Access) Development and aging of the cerebral cortex show similar topographic organization and are governed by the same genes. It is unclear whether the same is true for subcortical regions, which follow fundamentally different ontogenetic and phylogenetic principles. We tested the hypothesis that genetically governed neurodevelopmental processes can be traced throughout life by assessing to which degree brain regions that develop together continue to change together through life. Analyzing over 6000 longitudinal MRIs of the brain, we used graph theory to identify five clusters of coordinated development, indexed as patterns of correlated volumetric change in brain structures. The clusters tended to follow placement along the cranial axis in embryonic brain development, suggesting continuity from prenatal stages, and correlated with cognition. Across independent longitudinal datasets, we demonstrated that developmental clusters were conserved through life. Twin-based genetic correlations revealed distinct sets of genes governing change in each cluster. Single-nucleotide polymorphisms-based analyses of 38,127 cross-sectional MRIs showed a similar pattern of genetic volume-volume correlations. In conclusion, coordination of subcortical change adheres to fundamental principles of lifespan continuity and genetic organization. 22 p.

BackgroundInternalizing and externalizing psychopathology factors explain much of the covariance among psychiatric conditions, especially at the level of genetic risk. However, few studies have examined internalizing and externalizing factors in middle-aged samples, especially their ability to predict later symptoms across midlife. The goals of the current study were (i) to quantify the genetic and environmental influences on internalizing and externalizing psychopathology in individuals in their early 40s, and (ii) examine the extent to which these genetic and environmental influences predict self-reported measures of internalizing and externalizing symptoms 15–20 years later.Method1484 male twins completed diagnostic interviews of psychopathology at mean age 41 and self-reported measures of anxiety, depression, substance use, and related variables at up to two time-points in late middle age (mean ages 56 and 62).ResultsStructural equation modeling of the diagnostic interviews confirmed that internalizing and externalizing factors accounted for most of the genetic variance in individual disorders, with substantial genetic (ra = 0.70) and environmental (re = 0.77) correlations between the factors. Internalizing psychopathology at age 41 was correlated with latent factors capturing anxiety, depression, and/or post-traumatic stress symptoms at ages 56 (r = 0.51) and 62 (r = 0.43). Externalizing psychopathology at age 41 was correlated r = 0.67 with a latent factor capturing aggression, tobacco use, and alcohol use at age 56. Stability of both factors was driven by genetic influences.ConclusionsThese findings demonstrate the considerable stability of internalizing and externalizing psychopathology symptoms across middle age, especially their genetic influences. Diagnostic interviews effectively predict self-reported symptoms and behaviors 15–20 years later.

Mild cognitive impairment (MCI) is a heterogeneous condition with variable outcomes. Improving diagnosis to increase the likelihood that MCI reliably reflects prodromal Alzheimer’s Disease (AD) would be of great benefit for clinical practice and intervention trials. In 230 cognitively normal (CN) and 394 MCI individuals from the Alzheimer’s Disease Neuroimaging Initiative, we studied whether an MCI diagnostic requirement of impairment on at least two episodic memory tests improves 3-year prediction of medial temporal lobe atrophy and progression to AD. Based on external age-adjusted norms for delayed free recall on the Rey Auditory Verbal Learning Test (AVLT), MCI participants were further classified as having normal (AVLT+, above −1 SD, n = 121) or impaired (AVLT -, −1 SD or below, n = 273) AVLT performance. CN, AVLT+, and AVLT- groups differed significantly on baseline brain (hippocampus, entorhinal cortex) and cerebrospinal fluid (amyloid, tau, p-tau) biomarkers, with the AVLT- group being most abnormal. The AVLT- group had significantly more medial temporal atrophy and a substantially higher AD progression rate than the AVLT+ group (51% vs. 16%, p

Despite the relevance of semantic fluency measures to risk for dementia and psychiatric disorders, little is known about their genetic and environmental architecture in mid-to-late life. Participants represent 21,684 middle-aged and older adult twins (M = 60.84 years, SD = 11.21; Range 40-89) from six studies from three countries participating in the Interplay of Genes and Environment across Multiple Studies (IGEMS) consortium. All completed the same measure of semantic fluency (naming animals in 60 seconds). Results revealed small-to-moderate phenotypic associations with age and education, with education more strongly and positively associated with fluency performance in females than males. Heritability and environmental influences did not vary by age. Environmental variance was smaller with higher levels of education, but this effect was observed only in males. This is the largest study to examine the genetic and environmental architecture of semantic fluency, and the first to demonstrate that environmental influences vary based on levels of education.

Objective: Abnormal white matter (AWM) on magnetic resonance imaging is associated with cognitive performance in older adults. We explored cognitive associations with AWM during late-midlife. Method: Participants were community-dwelling men (n = 242; M = 61.90 years; range = 56-66). Linear-mixed effects regression models examined associations of total, periventricular, and deep AWM with cognitive performance, controlling for multiple comparisons. Models considering specific cognitive domains controlled for current general cognitive ability (GCA). We hypothesized that total AWM would be associated with worse processing speed, executive function, and current GCA; deep AWM would correlate with GCA and periventricular AWM would relate to specific cognitive abilities. We also assessed the potential influence of cognitive reserve by examining a moderation effect of early life (mean age of 20) cognition. Results: Greater total and deep AWM were associated with poorer current GCA. Periventricular AWM was associated with worse executive function, working memory, and episodic memory. When periventricular and deep AWM were modeled simultaneously, both retained their respective significant associations with cognitive performance. Cognitive reserve did not moderate associations. Conclusions: Our findings suggest that AWM contributes to poorer cognitive function in late-midlife. Examining only total AWM may obscure the potential differential impact of regional AWM. Separating total AWM into subtypes while controlling for current GCA revealed a dissociation in relationships with cognitive performance; deep AWM was associated with nonspecific cognitive ability whereas periventricular AWM was associated with specific frontal-related abilities and memory. Management of vascular or other risk factors that may increase the risk of AWM should begin during or before early late-midlife. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Growing research suggests that posttraumatic stress disorder (PTSD) may be a risk factor for poor cardiovascular health, and yet our understanding of who might be at greatest risk of adverse cardiovascular outcomes after trauma is limited. In this study, we conducted the first examination of the individual and synergistic contributions of PTSD symptoms and blood pressure genetics to continuous blood pressure levels. We harnessed the power of the Psychiatric Genomics Consortium-PTSD Physical Health Working Group and investigated these associations across 11 studies of 72,224 trauma-exposed individuals of European (n = 70,870) and African (n = 1,354) ancestry. Genetic contributions to blood pressure were modeled via polygenic scores (PGS) for systolic blood pressure (SBP) and diastolic blood pressure (DBP) that were derived from a prior trans-ethnic blood pressure genome-wide association study (GWAS). Results of trans-ethnic meta-analyses revealed significant main effects of the PGS on blood pressure levels [SBP: β = 2.83, standard error (SE) = 0.06, p < 1E-20; DBP: β = 1.32, SE = 0.04, p < 1E-20]. Significant main effects of PTSD symptoms were also detected for SBP and DBP in trans-ethnic meta-analyses, though there was significant heterogeneity in these results. When including data from the largest contributing study – United Kingdom Biobank – PTSD symptoms were negatively associated with SBP levels (β = −1.46, SE = 0.44, p = 9.8E-4) and positively associated with DBP levels (β = 0.70, SE = 0.26, p = 8.1E-3). However, when excluding the United Kingdom Biobank cohort in trans-ethnic meta-analyses, there was a nominally significant positive association between PTSD symptoms and SBP levels (β = 2.81, SE = 1.13, p = 0.01); no significant association was observed for DBP (β = 0.43, SE = 0.78, p = 0.58). Blood pressure PGS did not significantly moderate the associations between PTSD symptoms and blood pressure levels in meta-analyses. Additional research is needed to better understand the extent to which PTSD is associated with high blood pressure and how genetic as well as contextual factors may play a role in influencing cardiovascular risk.

Memory performance results from plasticity, the ability to change with experience. We show that benefit from practice over a few trials, learning slope, is predictive of long-term recall and hippocampal volume across a broad age range and a long period of time, relates to memory training benefit, and is heritable. First, in a healthy lifespan sample (n = 1825, age 4–93 years), comprising 3483 occasions of combined magnetic resonance imaging (MRI) scans and memory tests over a period of up to 11 years, learning slope across 5 trials was uniquely related to performance on a delayed free recall test, as well as hippocampal volume, independent from first trial memory or total memory performance across the five learning trials. Second, learning slope was predictive of benefit from memory training across ten weeks in an experimental subsample of adults (n = 155). Finally, in an independent sample of male twins (n = 1240, age 51–50 years), learning slope showed significant heritability. Within-session learning slope may be a useful marker beyond performance per se, being heritable and having unique predictive value for long-term memory function, hippocampal volume and training benefit across the human lifespan.

ImportanceBoth cognitive reserve and modifiable lifestyle behaviors are associated with dementia risk. The effect of early lifestyle behaviors and cognitive reserve on late midlife brain aging could inform early identification and risk reduction of future dementia.ObjectiveDetermine associations of young adult cognitive reserve, early midlife lifestyle behaviors, and the reserve-by-lifestyle interaction on late midlife brain age. Examine the relationship between mild cognitive impairment (MCI) and brain age.DesignParticipants were from the nationally representative Vietnam Era Twin Study of Aging (VETSA). Cognitive reserve was assessed at median age 20 years (IQR=1.38) with the Armed Forces Qualification Test (AFQT). Lifestyle behaviors (smoking, alcohol consumption, and social engagement) were assessed at median age 41 (IQR=5.00). Structural brain imaging conducted at median age 69 (IQR=4.74) was used to construct predicted brain age difference scores (PBAD=chronological age minus predicted brain age) and MCI was ascertained.SettingIn-person cognitive testing (ages 20 and 69); mailed survey (age 41); structural MRI, MCI diagnosis at University of California, San Diego (age 69).Participants431 community-dwelling men.ExposuresAFQT; self-reported health and lifestyle behaviors.Main outcomes and measuresPBAD scores; MCI.ResultsIn fully adjusted mixed linear models, age 20 cognitive reserve and the age 41 lifestyle composite were associated with age 69 PBAD [t (104)=2.62, p=0.01, 95%CI 0.874, 6.285; t (104)=3.37, p=0.001, 95%CI 0.583, 2.249 respectively] as was the reserve-by-lifestyle interaction [t (104) = −2.29, p=0.02, 95%CI −2.330, −0.167]. Unfavorable lifestyle predicted more advanced brain age, but only for those with lower young adult cognitive reserve. The MCI group had more advanced brain age (F (2,130) = 3.13; p=0.05).Conclusions and relevanceFavorable lifestyle behaviors promoted resistance to accelerated brain aging 3 decades later for those with lower young adult cognitive reserve. High reserve appeared to be protective regardless of lifestyle. The association with MCI suggests that advanced PBAD scores reflect poorer brain integrity, although it is unclear if PBAD is related to Alzheimer’s dementia specifically. Lower cognitive reserve increases risk for dementia, but early lifestyle modification may promote healthier brain aging and dementia risk reduction, particularly in those with lower reserve.Study TypeCohort StudyKey PointsQuestionDo modifiable lifestyle behaviors in early midlife predict later accelerated brain aging and is that association moderated by cognitive reserve?FindingsA lifestyle composite of smoking, alcohol consumption and social engagement at age 41 was associated with estimated brain age in late midlife. There was a significant moderation effect whereby more unfavorable lifestyle behaviors predicted more advanced brain aging, but only in those with low-to-moderate cognitive reserve.MeaningFavorable lifestyle behaviors appear to be protective for brain integrity especially among those with lower cognitive reserve. Early midlife efforts at prevention could be prioritized among those with lower cognitive reserve.

ObjectiveTo test the hypothesis that individual differences in episodic memory and verbal fluency in cognitively normal middle-aged adults will predict progression to amnestic mild cognitive impairment (MCI) after 6 years.MethodThe cohort analyzed included 842 male twins who were cognitively normal at baseline (mean 56 years) and completed measures of episodic memory and verbal fluency at baseline and again 6 years later (mean 62 years).ResultsPoor episodic memory predicted progression to both amnestic MCI (odds ratio [OR], 4.42; 95% confidence interval [CI], 2.44–10.60) and nonamnestic MCI (OR, 1.92; 95% CI, 1.32–3.44). Poor semantic verbal fluency also independently predicted progression to amnestic MCI (OR, 1.86; 95% CI, 1.12–3.52). In the full sample, a semantic-specific fluency latent variable at wave 1 (which controls for letter fluency) predicted change in episodic memory at wave 2 (β = 0.13), but not vice versa (β = 0.04). Associations between episodic memory and verbal fluency factors were primarily explained by genetic, rather than environmental, correlations.ConclusionsAmong individuals who were cognitively normal at wave 1, episodic memory moderately to strongly predicted progression to MCI at average age 62, emphasizing the fact that there is still meaningful variability even among cognitively normal individuals. Episodic memory, which is typically a primary focus for Alzheimer disease (AD) risk, declined earlier and more quickly than fluency. However, semantic fluency at average age 56 predicted 6-year change in memory as well as progression to amnestic MCI even after accounting for baseline memory performance. These findings emphasize the utility of memory and fluency measures in early identification of AD risk.

Background and Purpose: Periventricular white matter hyperintensities (WMH; PVWMH) and deep WMH (DWMH) are regional classifications of WMH and reflect proposed differences in cause. In the first study, to date, we undertook genome-wide association analyses of DWMH and PVWMH to show that these phenotypes have different genetic underpinnings. Methods: Participants were aged 45 years and older, free of stroke and dementia. We conducted genome-wide association analyses of PVWMH and DWMH in 26,654 participants from CHARGE (Cohorts for Heart and Aging Research in Genomic Epidemiology), ENIGMA (Enhancing Neuro-Imaging Genetics Through Meta-Analysis), and the UKB (UK Biobank). Regional correlations were investigated using the genome-wide association analyses -pairwise method. Cross-trait genetic correlations between PVWMH, DWMH, stroke, and dementia were estimated using LDSC. Results: In the discovery and replication analysis, for PVWMH only, we found associations on chromosomes 2 ( NBEAL ), 10q23.1 ( TSPAN14/FAM231A ), and 10q24.33 ( SH3PXD2A). In the much larger combined meta-analysis of all cohorts, we identified ten significant regions for PVWMH: chromosomes 2 (3 regions), 6, 7, 10 (2 regions), 13, 16, and 17q23.1. New loci of interest include 7q36.1 ( NOS3 ) and 16q24.2. In both the discovery/replication and combined analysis, we found genome-wide significant associations for the 17q25.1 locus for both DWMH and PVWMH. Using gene-based association analysis, 19 genes across all regions were identified for PVWMH only, including the new genes: CALCRL (2q32.1), KLHL24 (3q27.1), VCAN (5q27.1), and POLR2F (22q13.1). Thirteen genes in the 17q25.1 locus were significant for both phenotypes. More extensive genetic correlations were observed for PVWMH with small vessel ischemic stroke. There were no associations with dementia for either phenotype. Conclusions: Our study confirms these phenotypes have distinct and also shared genetic architectures. Genetic analyses indicated PVWMH was more associated with ischemic stroke whilst DWMH loci were implicated in vascular, astrocyte, and neuronal function. Our study confirms these phenotypes are distinct neuroimaging classifications and identifies new candidate genes associated with PVWMH only.

We have carried out meta-analyses of genome-wide association studies (GWAS) (n = 23 784) of the first two principal components (PCs) that group together cortical regions with shared variance in their surface area. PC1 (global) captured variations of most regions, whereas PC2 (visual) was specific to the primary and secondary visual cortices. We identified a total of 18 (PC1) and 17 (PC2) independent loci, which were replicated in another 25 746 individuals. The loci of the global PC1 included those associated previously with intracranial volume and/or general cognitive function, such as MAPT and IGF2BP1. The loci of the visual PC2 included DAAM1, a key player in the planar-cell-polarity pathway. We then tested associations with occupational aptitudes and, as predicted, found that the global PC1 was associated with General Learning Ability, and the visual PC2 was associated with the Form Perception aptitude. These results suggest that interindividual variations in global and regional development of the human cerebral cortex (and its molecular architecture) cascade—albeit in a very limited manner—to behaviors as complex as the choice of one’s occupation.

Trauma-exposed adults with high levels of posttraumatic stress symptoms (PTSS) report poorer health-related quality of life (HRQOL), but less is known about the persistence of this relationship over time. Participants from the Vietnam Era Twin Study of Aging reported on PTSS, health, and sociodemographic characteristics at average age 38, 775 participants reported having been exposed to trauma. Later, at average ages 56 and 62, mental and physical HRQOL were assessed with the Short-Form 36. Premorbid risk for anxiety/neuroticism was evaluated with a polygenic risk score derived from a large genome-wide association study meta-analysis. In multivariate mixed models, having higher levels of PTSS, poorer self-rated health, lower income, and less education at age 38 were associated with worse physical and mental HRQOL two decades later. Chronic health problems at age 38 predicted midlife physical but not mental HRQOL. Although genetic risk for neuroticism was correlated with HRQOL and PTSS, it was no longer significant in multivariate models. Health-related quality of life (HRQOL) predicts morbidity and mortality independently of objective health measures, early interventions may help to mitigate the ongoing impact of trauma on quality of life.

While development and aging of the cerebral cortex show a similar topographic organization and are mainly governed by the same genes, it is unclear whether the same is true for subcortical structures, which follow fundamentally different ontogenetic and phylogenetic principles than the cerebral cortex. To test the hypothesis that genetically governed neurodevelopmental processes can be traced in subcortical structures throughout life, we analyzed a longitudinal magnetic resonance imaging dataset (n = 974, age 4-89 years), identifying five clusters of longitudinal change in development. With some exceptions, these clusters followed placement along the cranial axis in embryonic brain development, suggesting continuity in the pattern of change from prenatal stages. Developmental change patterns were conserved through the lifespan and predicted general cognitive function in an age-invariant manner. The results were replicated in longitudinal data from the Lifebrain consortium (n = 756, age 19-83 years). Genetic contributions to longitudinal brain changes were calculated from the Vietnam Era Twin Study of Aging (n = 331 male twins, age 51-60 years), revealing that distinct sets of genes tended to govern change for each developmental cluster. This finding was confirmed with single nucleotide polymorphisms and cross-sectional MRI data from the UK Biobank (n = 20,588, age 40-69), demonstrating significantly higher co-heritability among structures belonging to the same developmental clusters. Together, these results suggest that coordination of subcortical change adheres to fundamental principles of lifespan continuity, genetic organization and age-invariant relationships to cognitive function.Significance statementHere we show that subcortical change during childhood development is organized in clusters. These clusters tend to follow the main gradient of embryonic brain development, and are stable across life. This means that subcortical regions changing together in childhood also change together throughout the rest of life, in accordance with a lifespan perspective on brain development and aging. Twin and single nucleotide polymorphism-based heritability analyses in middle-aged and older adults showed that volume and volume change of regions within each developmental cluster tended to be governed by the same sets of genes. Thus, volumetric changes across subcortical regions are tightly organized, and the coordinated change can be described in a lifespan perspective according to ontogenetic and genetic influences.

SummaryHuman brain structure changes throughout our lives. Altered brain growth or rates of decline are implicated in a vast range of psychiatric, developmental, and neurodegenerative diseases. Here, we identified common genetic variants that affect rates of brain growth or atrophy, in the first genome-wide association meta-analysis of changes in brain morphology across the lifespan. Longitudinal MRI data from 15,640 individuals were used to compute rates of change for 15 brain structures. The most robustly identified genesGPR139, DACH1andAPOEare associated with metabolic processes. We demonstrate global genetic overlap with depression, schizophrenia, cognitive functioning, insomnia, height, body mass index and smoking. Gene-set findings implicate both early brain development and neurodegenerative processes in the rates of brain changes. Identifying variants involved in structural brain changes may help to determine biological pathways underlying optimal and dysfunctional brain development and ageing.

Degree of memory impairment is often used to infer early versus late amnestic mild cognitive impairment (aMCI). Previously, 318 Alzheimer’s Disease Neuroimaging Initiative participants with aMCI – determined by a single memory test – were divided based on Rey Auditory Verbal Learning Task (AVLT) delayed recall: AVLT-impaired (n=225) and AVLT-normal (n=93). Equally consistent with differential progression or differential diagnosis, the AVLT-impaired group had more abnormal Alzheimer’s disease (AD) biomarkers, more neurodegeneration over time, and were more likely to develop AD. In the present study, higher AD polygenic risk scores were associated with increased odds of being AVLT-impaired (OR=1.8, P

The Vietnam Era Twin Study of Aging (VETSA) is a longitudinal behavioral genetic study with a primary focus on cognitive and brain aging in men, particularly early identification of risk for mild cognitive impairment (MCI) and Alzheimer’s disease (AD). It comprises a subset of over 1600 twins from the Vietnam Era Twin Registry. Twins live all over the USA. Assessments began when participants were in their 50s. Follow-ups were conducted every 5–6 years, and wave 3 has been completed as of this writing. The age range of participants is narrow (about 10 years). An extensive neurocognitive test battery has added precision in assessing differences in middle-aged adults, and predicting progression to MCI. Young adult cognitive test data (at an average age of 20 years) provide a means of disentangling aging effects from longstanding differences. Genome wide genotyping and plasma assays of AD biomarkers from waves 1 and 3 were conducted in wave 3. These features make the VETSA ideal for studying the heterogeneity of within-individual trajectories from midlife to old age, and for early detection of risk factors for cognitive decline.

Alzheimer's dementia (AD) begins many years before its clinical symptoms. Metabolic dysfunction represents a core feature of AD and cognitive impairment, but few metabolomic studies have focused on cognitive aging in midlife. Using an untargeted metabolomics approach, we identified metabolic predictors of cognitive aging in midlife using fasting plasma sample from 30 middle-aged (mean age 57.2), male-male twin pairs enrolled in the Vietnam Era Twin Study of Aging (VETSA). For all twin pairs, one twin developed incident MCI, whereas his co-twin brother remained to be cognitively normal during an average 5.5-year follow-up. Linear mixed model was used to identify metabolites predictive of MCI conversion or cognitive change over time, adjusting for traditional risk factors. Results from twins were replicated in an independent cohort of middle-aged adults (mean age 59.1) in the Wisconsin Registry for Alzheimer's Prevention (WRAP). Results in twins showed that higher baseline levels of four plasma metabolites, including sphingomyelin (d18:1/20:1 and d18:2/20:0), sphingomyelin (d18:1/22:1, d18:2/22:0, and d16:1/24:1), DAG (18:2/20:4), and hydroxy-CMPF, were significantly associated with a slower decrease in one or more domains of cognitive function. The association of sphingomyelin (d18:1/20:1 and d18:2/20:0) was replicated in WRAP. Our results support that metabolic perturbation occurs many years before cognitive impairment and plasma metabolites may serve as early biomarkers for prediction or monitoring of cognitive aging and AD in midlife.

INTRODUCTIONPredicting risk for Alzheimer’s disease when most people are likely still biomarker negative would aid earlier identification. We hypothesized that combining multiple memory tests and scores in middle-aged adults would provide useful, and non-invasive, prediction of 6-year progression to MCI.METHODSWe examined 849 men who were cognitively normal at baseline (mean age=55.69±2.45).RESULTSCalifornia Verbal Learning Test learning trials was the best individual predictor of amnestic MCI (OR=4.75). A latent factor incorporating 7 measures across 3 memory tests provided much stronger prediction (OR=9.88). This compared favorably with biomarker-based prediction in a study of much older adults.DISCUSSIONNeuropsychological tests are sensitive and early indicators of Alzheimer’s disease risk at an age when few individuals are likely to have yet become biomarker positive. Single best measures may appear time- and cost-effective, but 30 additional minutes of testing, and use of multiple scores within tests, provides substantially improved prediction

Loos, Ruth J F/0000-0002-8532-5087; Huibregtse, Brooke M./0000-0003-0977-7249; Kandler, Christian/0000-0002-9175-235X; Hjelmborg, Jacob/0000-0001-9630-9149; mangino, massimo/0000-0002-2167-7470; Siribaddana, Sisira/0000-0001-5821-2557; Plomin, Robert/0000-0002-0756-3629; Latvala, Antti/0000-0001-5695-117X; Kaprio, Jaakko/0000-0002-3716-2455; Willemsen, Gonneke/0000-0003-3755-0236; Tan, Qihua/0000-0003-3194-0030; Pahlen, Shandell/0000-0003-0753-4155; Pedersen, Nancy/0000-0001-8057-3543; Haworth, Claire/0000-0002-8608-289X; Nistico, Lorenza/0000-0003-1805-6240; Skytthe, Axel/0000-0002-8629-4913; van Beijsterveldt, Toos/0000-0002-6617-4201; Rebato, Esther/0000-0003-1221-8501; Li, Lintao/0000-0002-0604-9660; Bartels, Meike/0000-0002-9667-7555; Silventoinen, Karri/0000-0003-1759-3079; Cunha Oliveira, Vinicius/0000-0002-8658-3774; Sund, Reijo/0000-0002-6268-8117; Sodemann, Morten/0000-0001-8992-2500; Rasmussen, Finn/0000-0001-7915-7809; Harden, Kathryn/0000-0002-1557-6737; Medda, Emanuela/0000-0003-4837-4549; Kyvik, Kirsten Ohm/0000-0003-2981-0245; Colodro Conde, Lucia/0000-0002-9004-364X WOS: 000517442200060 PubMed: 31364586 The COllaborative project of Development of Anthropometrical measures in Twins (CODATwins) project is a large international collaborative effort to analyze individual-level phenotype data from twins in multiple cohorts from different environments. The main objective is to study factors that modify genetic and environmental variation of height, body mass index (BMI, kg/m(2)) and size at birth, and additionally to address other research questions such as long-term consequences of birth size. The project started in 2013 and is open to all twin projects in the world having height and weight measures on twins with information on zygosity. Thus far, 54 twin projects from 24 countries have provided individual-level data. The CODATwins database includes 489,981 twin individuals (228,635 complete twin pairs). Since many twin cohorts have collected longitudinal data, there is a total of 1,049,785 height and weight observations. For many cohorts, we also have information on birth weight and length, own smoking behavior and own or parental education. We found that the heritability estimates of height and BMI systematically changed from infancy to old age. Remarkably, only minor differences in the heritability estimates were found across cultural-geographic regions, measurement time and birth cohort for height and BMI. In addition to genetic epidemiological studies, we looked at associations of height and BMI with education, birth weight and smoking status. Within-family analyses examined differences within same-sex and opposite-sex dizygotic twins in birth size and later development. The CODATwins project demonstrates the feasibility and value of international collaboration to address gene-by-exposure interactions that require large sample sizes and address the effects of different exposures across time, geographical regions and socioeconomic status. Academy of FinlandAcademy of Finland [266592, 100499, 205585, 118555, 141054, 265240, 263278, 264146]; Osaka University's International Joint Research Promotion Program; Centre of Research Excellence Grant from the National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [1079102]; National Institutes of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01 HD068435, R01 MH062375, 1R01ESO15150-01, R21 AG039572]; National Medical Research Council Research Fellowship; California Tobacco-Related Disease Research ProgramUniversity of California System [7RT-0134H, 8RT-0107H, 6RT-0354H]; National Institute on AgingUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Aging (NIA) [1RO1-AG13662-01A2]; Swedish Research Council through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) [340-2013-5867]; Swedish Heart-Lung FoundationSwedish Heart-Lung Foundation; Swedish Asthma and Allergy Association's Research Foundation; Special Fund for Health Scientific Research in the Public Welfare, China [201502006]; NIDAUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Drug Abuse (NIDA) [DA011015, HD10333]; National Program for Research Infrastructure 2007 from the Danish Agency for Science, Technology and Innovation; Research Council for Health and Disease; Velux Foundation; US National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [P01 AG08761]; Fund of Scientific Research, FlandersFWO; ENGAGE - European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007 [201413]; National Institute of Alcohol Abuse and AlcoholismUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute on Alcohol Abuse & Alcoholism (NIAAA) [AA-12502, AA-00145, AA-09203]; Academy of Finland Center of Excellence in Complex Disease GeneticsAcademy of Finland [213506, 129680]; Cancer Research UKCancer Research UK [C1418/A7974]; W T Grant Foundation; University of London Central Research fund; Medical Research CouncilMedical Research Council UK (MRC) [G81/343, G120/635]; Economic and Social Research CouncilEconomic & Social Research Council (ESRC) [RES-000-22-2206]; Institute of Social Psychiatry [06/07-11]; Leverhulme Research FellowshipLeverhulme Trust [RF/2/RFG/2008/0145]; Goldsmiths, University of London; UK Medical Research CouncilMedical Research Council UK (MRC) [MR/M021475/1, G0901245]; National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London; NIHUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [R01AG053217, RC2 HL103416]; National Natural Science Foundation of ChinaNational Natural Science Foundation of China [81125007]; Global Research Network Program of the National Research Foundation [NRF 2011-220-E00006]; European Research Council (ERC)European Research Council (ERC) [240994]; Michigan State University; National Institute of Mental Health (NIMH)United States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01-MH081813, R01-MH0820-54, R01-MH092377-02, R21-MH070542-01, R03-MH63851-01, 1R01-MH118848-01]; Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) [R01-HD066040]; MSU Foundation [11-SPG-2518]; Fundacion Seneca, Regional Agency for Science and Technology, Murcia, Spain [08633/PHCS/08, 15302/PHCS/10, 19479/PI/14]; Ministry of Science and Innovation, SpainSpanish Government [PSI2009-11560, PSI2014-56680-R]; MagW/ZonMW [904-61-090, 985-10-002, 912-10-020, 904-61-193, 480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192]; European Research CouncilEuropean Research Council (ERC) [ERC-230374]; JSPS KAKENHI JP [23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261]; Australian National Health and Medical Research CouncilNational Health and Medical Research Council of Australia [437015, 607358]; Bonnie Babes Foundation [BBF20704]; Financial Markets Foundation for Children [032-2007]; Victorian Government; Portuguese agency for research (The Foundation for Science and Technology [FCT]) [POCI/DES/56834/2004]; Wellcome TrustWellcome Trust; Medical Research CouncilMedical Research Council UK (MRC); European UnionEuropean Union (EU); National Institute for Health Research (NIHR)National Institute for Health Research (NIHR); King's College London; Fonds Quebecois de la Recherche sur la Societe et la CultureFQRNT; Fonds de la Recherche en Sante du QuebecFonds de la Recherche en Sante du Quebec; Social Science and Humanities Research Council of Canada; National Health Research Development Program; Canadian Institutes for Health ResearchCanadian Institutes of Health Research (CIHR); Sainte-Justine Hospital's Research Center; Canada Research Chair ProgramCanada Research Chairs; National Research Foundation of KoreaNational Research Foundation of Korea [NRF-371-2011-1 B00047]; Swedish Research CouncilSwedish Research Council [2017-00641]; UK Biotechnology and Biological Sciences Research CouncilBiotechnology and Biological Sciences Research Council (BBSRC) [31/D19086]; MRCMedical Research Council UK (MRC) [MR/M021475/1]; Krkkale University [2009/43]; TUBITAKTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) [114C117]; National Institute of Mental HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USANIH National Institute of Mental Health (NIMH) [R01 MH58354]; National Institute of HealthUnited States Department of Health & Human ServicesNational Institutes of Health (NIH) - USA [NIA R01 AG018384, R01 AG018386, R01 AG022381, R01 AG022982]; VA San Diego Center of Excellence for Stress and Mental Health; Cooperative Studies Program of the Office of Research & Development of the United States Department of Veterans Affairs; Japan Society for the Promotion of ScienceMinistry of Education, Culture, Sports, Science and Technology, Japan (MEXT)Japan Society for the Promotion of Science [15H05105]; Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust; Stockholm County Council (ALF-projects)Stockholm County Council; Twins, a nonprofit Association for Scientific Research in Multiple Births (Belgium); [5T32DA017637]; [5T32AG052371] This study was conducted within the CODATwins project (Academy of Finland #266592). K Silventoinen is supported by Osaka University's International Joint Research Promotion Program. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (ID: 1079102), from the National Health and Medical Research Council. The Boston University Twin Project is funded by grants (#R01 HD068435 #R01 MH062375) from the National Institutes of Health to K. Saudino. Paulo Ferreira is funded by a National Medical Research Council Research Fellowship. California Twin Program was supported by The California Tobacco-Related Disease Research Program (7RT-0134H, 8RT-0107H, 6RT-0354H) and the National Institutes of Health (1R01ESO15150-01).; The Carolina African American Twin Study of Aging (CAATSA) was funded by a grant from the National Institute on Aging (grant 1RO1-AG13662-01A2) to K. E. Whitfield. The CATSS-Study is supported by the Swedish Research Council through the Swedish Initiative for Research on Microdata in the Social And Medical Sciences (SIMSAM) framework grant no 340-2013-5867, grants provided by the Stockholm County Council (ALF-projects), the Swedish Heart-Lung Foundation and the Swedish Asthma and Allergy Association's Research Foundation.; Chinese National Twin Registry is funded by Special Fund for Health Scientific Research in the Public Welfare (Project No: 201502006), China. Colorado Twin Registry is funded by NIDA-funded center grant DA011015, & Longitudinal Twin Study HD10333; Author Huibregtse is supported by 5T32DA017637 and 5T32AG052371. Danish Twin Registry is supported by the National Program for Research Infrastructure 2007 from the Danish Agency for Science, Technology and Innovation, The Research Council for Health and Disease, the Velux Foundation and the US National Institute of Health (P01 AG08761). Since its origin, the East Flanders Prospective Survey has been partly supported by grants from the Fund of Scientific Research, Flanders and Twins, a nonprofit Association for Scientific Research in Multiple Births (Belgium). Data collection and analyses in Finnish twin cohorts have been supported by ENGAGE - European Network for Genetic and Genomic Epidemiology, FP7-HEALTH-F4-2007, grant agreement number 201413, National Institute of Alcohol Abuse and Alcoholism (grants AA-12502, AA-00145 and AA-09203 to R J Rose, the Academy of Finland Center of Excellence in Complex Disease Genetics (grant numbers: 213506, 129680), and the Academy of Finland (grants 100499, 205585, 118555, 141054, 265240, 263278 and 264146 to J Kaprio). Gemini was supported by a grant from Cancer Research UK (C1418/A7974). Waves 1-3 of Genesis 12-19 were funded by the W T Grant Foundation, the University of London Central Research fund and a Medical Research Council Training Fellowship (G81/343) and Career Development Award (G120/635) to Thalia C. Eley. Wave 4 was supported by grants from the Economic and Social Research Council (RES-000-22-2206) and the Institute of Social Psychiatry (06/07-11) to Alice M. Gregory, who was also supported at that time by a Leverhulme Research Fellowship (RF/2/RFG/2008/0145). Wave 5 was supported by funding to Alice M. Gregory from Goldsmiths, University of London. T. C. Eley is partly funded by a program grant from the UK Medical Research Council (MR/M021475/1).; This study presents independent research [partly] funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. The Minnesota Twin Registry (MTR) acknowledges support from NIH grant R01AG053217. Guangzhou Twin Eye Study is supported by National Natural Science Foundation of China (grant #81125007). Anthropometric measurements of the Hungarian twins were supported by Medexpert Ltd., Budapest, Hungary. Korean Twin-Family Register was supported by the Global Research Network Program of the National Research Foundation (NRF 2011-220-E00006). Longitudinal Israeli Study of Twins was funded by the Starting Grant no. 240994 from the European Research Council (ERC) to Ariel Knafo. The Michigan State University Twin Registry has been supported by Michigan State University, as well as grants R01-MH081813, R01-MH0820-54, R01-MH092377-02, R21-MH070542-01, R03-MH63851-01 and 1R01-MH118848-01 from the National Institute of Mental Health (NIMH), R01-HD066040 from the Eunice Kennedy Shriver National Institute for Child Health and Human Development (NICHD) and 11-SPG-2518 from the MSU Foundation. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIMH, the NICHD or the National Institutes of Health. The Murcia Twin Registry is supported by Fundacion Seneca, Regional Agency for Science and Technology, Murcia, Spain (08633/PHCS/08, 15302/PHCS/10 & 19479/PI/14) and Ministry of Science and Innovation, Spain (PSI2009-11560 & PSI2014-56680-R). The NAS-NRC Twin Registry acknowledges financial support from the National Institutes of Health grant number R21 AG039572. Netherlands Twin Register acknowledges the Netherlands Organization for Scientific Research (NWO) and MagW/ZonMW grants 904-61-090, 985-10-002, 912-10-020, 904-61-193,480-04-004, 463-06-001, 451-04-034, 400-05-717, Addiction-31160008, Middelgroot-911-09-032, Spinozapremie 56-464-14192; VU University's Institute for Health and Care Research (EMGO+); the European Research Council (ERC-230374), the Avera Institute, Sioux Falls, South Dakota (USA). Osaka University Aged Twin Registry is supported by grants from JSPS KAKENHI JP (23593419, 24792601, 26671010, 24590695, 26293128, 16K15385, 16K15978, 16K15989, 16H03261). PETS was supported by grants from the Australian National Health and Medical Research Council (grant numbers 437015 and 607358 to JC, and RS), the Bonnie Babes Foundation (grant number BBF20704 to JMC), the Financial Markets Foundation for Children (grant no. 032-2007 to JMC), and by the Victorian Government's Operational Infrastructure Support Program.; Madeira data comes from the following project: Genetic and environmental influences on physical activity, fitness and health: the Madeira family study Project reference: POCI/DES/56834/2004 Founded by the Portuguese agency for research (The Foundation for Science and Technology [FCT]). TwinsUK receives funding from the Wellcome Trust, Medical Research Council and European Union.; TwinsUK and M. Mangino are supported by the National Institute for Health Research (NIHR)-funded BioResource, Clinical Research Facility and Biomedical Research Centre based at Guy's and St Thomas' NHS Foundation Trust in partnership with King's College London. The Quebec Newborn Twin Study acknowledges financial support from the Fonds Quebecois de la Recherche sur la Societe et la Culture, the Fonds de la Recherche en Sante du Quebec, the Social Science and Humanities Research Council of Canada, the National Health Research Development Program, the Canadian Institutes for Health Research, Sainte-Justine Hospital's Research Center, and the Canada Research Chair Program (Michel Boivin). South Korea Twin Registry is supported by National Research Foundation of Korea (NRF-371-2011-1 B00047). We acknowledge The Swedish Twin Registry for access to data. The Swedish Twin Registry is managed by Karolinska Institutet and receives funding through the Swedish Research Council under the grant no. 2017-00641.; The Twins Early Development Study (TEDS) is supported by a program grant (G0901245) from the UK Medical Research Council and the work on obesity in TEDS is supported in part by a grant from the UK Biotechnology and Biological Sciences Research Council (31/D19086). Currently TEDS is supported by MRC grant 'MR/M021475/1'. The Texas Twin Project is currently funded by grant R01HD083613 from the National Institutes of Health. S. Y. oncel and F. Aliev are supported by Krkkale University Research Grant: KKU, 2009/43 and TUBITAK grant 114C117. The University of Southern California Twin Study is funded by a grant from the National Institute of Mental Health (R01 MH58354). Washington State Twin Registry (formerly the University of Washington Twin Registry) was supported in part by grant NIH RC2 HL103416 (D. Buchwald, PI). Vietnam Era Twin Study of Aging was supported by National Institute of Health grants NIA R01 AG018384, R01 AG018386, R01 AG022381 and R01 AG022982, and, in part, with resources of the VA San Diego Center of Excellence for Stress and Mental Health. The Cooperative Studies Program of the Office of Research & Development of the United States Department of Veterans Affairs has provided financial support for the development and maintenance of the Vietnam Era Twin (VET) Registry. The content of this paper is solely the responsibility of the authors and does not necessarily represent the official views of the NIA/NIH, or the VA. The West Japan Twins and Higher Order Multiple Births Registry was supported by Grant-in-Aid for Scientific Research (B) (grant number 15H05105) from the Japan Society for the Promotion of Science.

Height and general cognitive ability are positively associated, but the underlying mechanisms of this relationship are not well understood. Both height and general cognitive ability are positively associated with brain size. Still, the neural substrate of the height-cognitive ability association is unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to investigate whether the association between height and cognitive ability is mediated by cortical size. In addition to cortical volume, we used genetically, ontogenetically and phylogenetically distinct cortical metrics of total cortical surface area and mean cortical thickness. Height was positively associated with general cognitive ability and total cortical volume and cortical surface area, but not with mean cortical thickness. Mediation models indicated that the well-replicated height-general cognitive ability association is accounted for by individual differences in total cortical volume and cortical surface area (highly heritable metrics related to global brain size), and that the genetic association between cortical surface area and general cognitive ability underlies the phenotypic height-general cognitive ability relationship.

Early identification of younger, non-demented adults at elevated risk for Alzheimer's disease (AD) is crucial because the pathological process begins decades before dementia onset. Toward that end, we showed that an AD polygenic risk score (PRS) could identify mild cognitive impairment (MCI) in adults who were only in their 50s. Participants were 1176 white, non-Hispanic community-dwelling men of European ancestry in the Vietnam Era Twin Study of Aging (VETSA): 7% with amnestic MCI (aMCI); 4% with non-amnestic MCI (naMCI). Mean age was 56 years, with 89%

Animal studies have suggested essential links between chronic pain and the locus coeruleus (LC), a brainstem region important for attention, but also pain and cognitive modulation. Recently developed MRI sequences now allow us to assess LC integrity in vivo. Our study examined the association between late midlife chronic pain and LC integrity in older adults via neuromelanin-contrast imaging. For this study, 484 community-dwelling men participating in the third wave of the Vietnam Era Twin Study of Aging (mean age=67.55; SD=2.60) underwent MRI-assessed neuromelanin signal detection in the LC. Contrast-to-noise ratios between LC regions and a pontine tegmentum reference region were used to assess LC integrity. The LC was subdivided into rostral and caudal portions. Rostral LC is more cortical-projecting, while caudal LC projects more to the cerebellum and spinal cord. Reporting of pain intensity on the SF-36 Bodily Pain scale from all three study waves was used to classify those with chronic pain, defined as moderate to severe pain across all three waves (n = 80, 16.5%). Bootstrapped multivariable regression models examined associations between late midlife chronic pain and LC integrity, adjusting for age, depressive symptoms, medical comorbidities, opioid usage, and twin clustering. Late midlife chronic pain was associated with .13 standard deviation lower rostral LC integrity (95%CI: -.63 to -.07). Meanwhile, late midlife chronic pain was associated with .12 standard deviation lower caudal LC integrity (95%CI: -.70 to -.21). Chronic pain across late midlife was associated with greater LC integrity in older adults. Pain was not differentially associated with rostral or caudal LC. Future work should seek to understand mechanisms underlying LC-pain associations in older adults, especially regarding the roles of inflammation and accumulating pathology. R01AG050595.

Exposure to harmful substances and chemicals such as tobacco smoke, chemicals (e.g., herbicides, pesticides, Agent Orange) and metal dust has been associated with increased risk of developing cancer, cardiovascular disease, and other diseases that contribute to shorter life expectancy. Associations with brain health in relation to these exposures are less well studied. We examined the relationship between brain health and prolonged exposure to different harmful substances in 498 male participants average age 68 (range 61 to 73) from the Vietnam Era Twin Study of Aging (VETSA). For self-reported tobacco smoke, herbicides/pesticides, and metal dust we created three groups reflecting recency of exposure (current/former/never). For Agent Orange we examined two exposure groups (ever/never). Brain health, defined as predicted brain age (PBAD), was evaluated by applying Brain-Age Regression Analysis and Computation Utility software (BARACUS) to magnetic resonance images collected at age 68. Tobacco smoking (r=-0.15, p=0.0004 ) was significantly correlated with PBAD and remained significant (F=5.56, p=0.005) in multivariate analyses adjusted for age, socioeconomic status (SES), age 20 general cognitive ability, and non-independence of twins within pairs. Never smokers had significantly younger brains than current or former smokers. PBAD did not differ for current versus former smokers. In other analyses, more advanced PBAD was associated with non-amnestic MCI. In this sample, tobacco smoking had the strongest relationship with overall brain health in late midlife compared with other types of environmental exposures, reinforcing its role in pathological aging and its importance as a public health priority.

Recent work on procrastination has begun to unravel the genetic and environmental correlates of this problematic behavior. However, little is known about how strongly procrastination is associated with internalizing and externalizing psychopathology, and the extent to which shared genetic/environmental factors or relevant personality constructs (e.g., fear of failure, impulsivity, and neuroticism) can inform the structure of these associations. The current study examined data from 764 young adult twins who completed questionnaires assessing procrastination and personality and structured interviews regarding psychopathology symptoms. Results indicated that procrastination was positively correlated with both internalizing and externalizing latent variables and that these correlations were driven by shared genetic influences. Moreover, the association between procrastination and internalizing was accounted for by fear of failure and neuroticism, whereas the association between procrastination and externalizing was primarily explained by impulsivity. The role of procrastination in psychopathology is discussed using a framework that highlights common and broadband-specific variance.

Structural brain changes that occur during development and ageing are related to mental health and general cognitive functioning. Individuals differ in the extent to which their brain volumes change over time, but whether these differences can be attributed to differences in their genotypes has not been widely studied. Here we estimate heritability (h2) of changes in global and subcortical brain volumes in five longitudinal twin cohorts from across the world and in different stages of the lifespan (N = 861). Heritability estimates of brain changes were significant and ranged from 16% (caudate) to 42% (cerebellar gray matter) for all global and most subcortical volumes (with the exception of thalamus and pallidum). Heritability estimates of change rates were generally higher in adults than in children suggesting an increasing influence of genetic factors explaining individual differences in brain structural changes with age. In children, environmental influences in part explained individual differences in developmental changes in brain structure. Multivariate genetic modeling showed that genetic influences of change rates and baseline volume significantly overlapped for many structures. The genetic influences explaining individual differences in the change rate for cerebellum, cerebellar gray matter and lateral ventricles were independent of the genetic influences explaining differences in their baseline volumes. These results imply the existence of genetic variants that are specific for brain plasticity, rather than brain volume itself. Identifying these genes may increase our understanding of brain development and ageing and possibly have implications for diseases that are characterized by deviant developmental trajectories of brain structure.

Increased mean diffusivity (MD) is hypothesized to reflect tissue degeneration and may provide subtle indicators of neuropathology as well as age-related brain changes in the absence of volumetric differences. Our aim was to determine the degree to which genetic and environmental variation in subcortical MD is distinct from variation in subcortical volume. Data were derived from a sample of 387 male twins (83 MZ twin pairs, 55 DZ twin pairs, and 111 incomplete twin pairs) who were MRI scanned as part of the Vietnam Era Twin Study of Aging. Quantitative estimates of MD and volume for 7 subcortical regions were obtained: thalamus, caudate nucleus, putamen, pallidum, hippocampus, amygdala, and nucleus accumbens. After adjusting for covariates, bivariate twin models were fitted to estimate the size and significance of phenotypic, genotypic, and environmental correlations between MD and volume at each subcortical region. With the exception of the amygdala, familial aggregation in MD was entirely explained by additive genetic factors across all subcortical regions with estimates ranging from 46 to 84%. Based on bivariate twin modeling, variation in subcortical MD appears to be both genetically and environmentally unrelated to individual differences in subcortical volume. Therefore, subcortical MD may be an alternative biomarker of brain morphology for complex traits worthy of future investigation. Hum Brain Mapp 38:2589-2598, 2017. © 2017 Wiley Periodicals, Inc.

Magnetic resonance imaging (MRI) has become an important tool in the early detection of age-related and neuropathological brain changes. Recent studies suggest that changes in mean diffusivity (MD) of cortical gray matter derived from diffusion MRI scans may be useful in detecting early effects of Alzheimer’s disease (AD), and that these changes may be detected earlier than alterations associated with standard structural MRI measures such as cortical thickness. Thus, due to its potential clinical relevance, we examined the genetic and environmental influences on cortical MD in middle-aged men to provide support for the biological relevance of this measure and to guide future gene association studies. It is not clear whether individual differences in cortical MD reflect neuroanatomical variability similarly detected by other MRI measures, or whether unique features are captured. For instance, variability in cortical MD may reflect morphological variability more commonly measured by cortical thickness. Differences among individuals in cortical MD may also arise from breakdowns in myelinated fibers running through the cortical mantle. Thus, we investigated whether genetic influences on variation in cortical MD are the same or different from those influencing cortical thickness and MD of white matter (WM) subjacent to the cortical ribbon. Univariate twin analyses indicated that cortical MD is heritable in the majority of brain regions; the average of regional heritability estimates ranged from 0.38 in the cingulate cortex to 0.66 in the occipital cortex, consistent with the heritability of other MRI measures of the brain. Trivariate analyses found that, while there was some shared genetic variance between cortical MD and each of the other two measures, this overlap was not complete (i.e., the correlation was statistically different from 1). A significant amount of distinct genetic variance influences inter-individual variability in cortical MD; therefore, this measure could be useful for further investigation in studies of neurodegenerative diseases and gene association studies.

There is evidence that differences among individuals in white matter microstructure, as measured with diffusion tensor imaging (DTI), are under genetic control. However, little is known about the relative contribution of genetic and environmental effects on different diffusivity indices among late middle-aged adults. Here, we examined the magnitude of genetic influences for fractional anisotropy (FA), and mean (MD), axial (AD), and radial (RD) diffusivities in male twins aged 56-66 years old. Using an atlas-based registration approach to delineate individual white matter tracts, we investigated mean DTI-based indices within the corpus callosum, 12 bilateral tracts and all these regions of interest combined. All four diffusivity indices had high heritability at the global level (72%-80%). The magnitude of genetic effects in individual tracts varied from 0% to 82% for FA, 0% to 81% for MD, 8% to 77% for AD, and 0% to 80% for RD with most of the tracts showing significant heritability estimates. Despite the narrow age range of this community-based sample, age was correlated with all four diffusivity indices at the global level. In sum, all diffusion indices proved to have substantial heritability for most of the tracts and the heritability estimates were similar in magnitude for different diffusivity measures. Future studies could aim to discover the particular set of genes that underlie the significant heritability of white matter microstructure. Hum Brain Mapp 38:2026-2036, 2017. © 2017 Wiley Periodicals, Inc.

The risk of posttraumatic stress disorder (PTSD) following trauma is heritable, but robust common variants have yet to be identified. In a multi-ethnic cohort including over 30,000 PTSD cases and 170,000 controls we conduct a genome-wide association study of PTSD. We demonstrate SNP-based heritability estimates of 5–20%, varying by sex. Three genome-wide significant loci are identified, 2 in European and 1 in African-ancestry analyses. Analyses stratified by sex implicate 3 additional loci in men. Along with other novel genes and non-coding RNAs, a Parkinson’s disease gene involved in dopamine regulation, PARK2, is associated with PTSD. Finally, we demonstrate that polygenic risk for PTSD is significantly predictive of re-experiencing symptoms in the Million Veteran Program dataset, although specific loci did not replicate. These results demonstrate the role of genetic variation in the biology of risk for PTSD and highlight the necessity of conducting sex-stratified analyses and expanding GWAS beyond European ancestry populations., Post-traumatic stress disorder (PTSD) is a common mental health problem. Here, the authors report a GWAS from the Psychiatric Genomics Consortium in which they identify two risk loci in European ancestry and one locus in African ancestry individuals and find that PTSD is genetically correlated with several other psychiatric traits.

Pathological changes in Alzheimer’s disease (AD) begin decades before dementia onset. Because locus coeruleus tau pathology is the earliest occurring AD pathology, targeting indicators of locus coeruleus (dys)function may improve midlife screening for earlier identification of AD risk. Pupillary responses during cognitive tasks are driven by the locus coeruleus and index cognitive effort. Several findings suggest task-associated pupillary response as an early marker of AD risk. Requiring greater effort suggests being closer to one’s compensatory capacity, and adults with mild cognitive impairment (MCI) have greater pupil dilation during digit span tasks than cognitively normal individuals, despite equivalent task performance. Higher AD polygenic risk scores (AD-PRSs) are associated with increased odds of MCI and tau positivity. We hypothesized that AD-PRSs would be associated with pupillary responses in cognitively normal middle-aged adults. We demonstrated that pupillary responses during digit span tasks were heritable (h2=.30-.36) in 1119 men ages 56-66. We then examined associations between AD-PRSs and pupillary responses in a cognitively normal subset who all had comparable span capacities (n=539). Higher AD-PRSs were associated with greater pupil dilation/effort in a high (9-digit recall) cognitive load condition; Cohen’s d=.36 for the upper versus lower quartile of the AD-PRS distribution. Results held up after controlling for APOE genotype. The results support pupillary response—and by inference, locus coeruleus dysfunction—as a genetically-mediated biomarker of early MCI/AD risk. In some studies, cognition predicted disease progression earlier than biomarkers. Pupillary responses might improve screening and early identification of genetically at-risk individuals even before cognitive performance declines.

BackgroundPTSD and depression commonly co-occur and have been associated with smaller hippocampal volumes compared to healthy and trauma-exposed controls. However, the hippocampus is heterogeneous, with subregions that may be uniquely affected in individuals with PTSD and depression.MethodsWe used random effects regressions and a harmonized neuroimaging protocol based on FreeSurfer (v6.0) to identify sub-structural hippocampal markers of current PTSD (C-PTSD), depression, and the interaction of these conditions across 31 cohorts worldwide (N=3,115;Mage=38.9±13.9 years). Secondary analyses tested these associations by sex and after modeling the simultaneous effects of remitted PTSD, childhood trauma, mild traumatic brain injury, and alcohol use disorder.ResultsA significant negative main effect of depression (n=800, vs. no depression, n=1456) was observed in the hippocampal tail (ß=−0.13) and CA1 (ß=−0.09) after adjusting for covariates and multiple testing (adjusted p’s (q)=0.028). A main effect of C-PTSD (n=1042 vs. control, n=1359) was not significant, but an interaction between C-PTSD and depression was significant in the CA1 (ß=−0.24, q=0.044). Pairwise comparisons revealed significantly smaller CA1 volumes in individuals with C-PTSD+Depression than controls (ß=−0.12, q=0.012), C-PTSD-only (ß=−0.17, q=0.001), and Depression-only (ß=−0.18, q=0.023). Follow-up analyses revealed sex effects in the hippocampal tail of depressed females, and an interaction effect of C-PTSD and depression in the fimbria of males.ConclusionsCollectively our results suggest that depression is a stronger predictor of hippocampal volumetry than PTSD, particularly in the CA1, and provide compelling evidence of more pronounced hippocampal phenotypes in comorbid PTSD and depression compared to either condition alone.

We conducted a genome-wide association meta-analysis of two ischemic white matter disease subtypes in the brain, periventricular and deep white matter hyperintensities (PVWMH and DWMH). In 26,654 participants, we found 10 independent genome-wide significant loci only associated with PVWMH, four of which have not been described previously for total WMH burden (16q24.2, 17q21.31, 10q23.1, 7q36.1). Additionally, in both PVWMH and DWMH we observed the previous association of the 17q25.1 locus with total WMH. We found that both phenotypes have shared but also distinct genetic architectures, consistent with both different underlying and related pathophysiology. PVWMH had more extensive genetic overlap with small vessel ischemic stroke, and unique associations with several loci implicated in ischemic stroke. DWMH were characterized by associations with loci previously implicated in vascular as well as astrocytic and neuronal function. Our study confirms the utility of these phenotypes and identifies new candidate genes associated only with PVWMH.